Schemas Tables Indexes Views and JoOracle Objects ins

8/22/2019 Schemas Tables Indexes Views and JoOracle Objects ins

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Schema

Tables

Indexes

Views

Joins

Oracle Objects


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Schema A schema is a collection of database objects like tables, indexes, sequences, procedures,

functions, packages, etc.

A schema is created when a user is created and dropped when the user is dropped.

Each user has only one schema and a schema is owned by only one user. ie. There is one-to-one

mapping between schemas and users.

The schema is named after the user who owns it.

Users can access objects across the schemas if the privileges are granted.

Users objects are stored entirely in side a single schema and never span across schemas.

Tables

Indexes

Sequences

Views

procedures

Packages

Functions

Schema A

Tables

Indexes

Sequences

Views

procedures

Packages

Functions

Schema B


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Different types of Tables

Heap

Index Organized

Clustered


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ROW ID 1 FORD Marketing Mumbai

ROW ID 2 JONES Manufacturing Chennai

ROW ID 3 MILLER Finance Chennai

ROW ID 4 JAMES Manufacturing Mumbai

ROW ID 5 BLAKE Marketing Chennai

ROW ID 6 ADAMS Manufacturing Mumbai

ROW ID 7 SCOTT Manufacturing Hyderabad

ROW ID 8 SMITH Finance Chennai

ROW ID 9 TURNER Administration Mumbai

ROW ID 10 KING Manufacturing Mumbai

ROW ID 11 ALLEN Finance Chennai

ROW ID 12 MARTIN Manufacturing Hyderabad

ROW ID 13 WARD Marketing Mumbai

ROW ID 14 CLARK Manufacturing Chennai

Structure of Heap Table


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Row ID 6 ADAMS Manufacturing Mumbai

Row ID 7 ALLEN Finance Chennai

Row ID 8 BLAKE Marketing Chennai

Row ID 9 CLARK Manufacturing Chennai

Row ID 12 FORD Marketing Mumbai

Row ID 13 JAMES Manufacturing Chennai

Row ID 14 JONES Manufacturing Chennai

Row ID 15 KING Manufacturing Chennai

Row ID 45 MARTIN Manufacturing Hyderabad

Row ID 46 MILLER Finance Chennai

Row ID 47 SCOTT Manufacturing Hyderabad

Row ID 67 SMITH Finance Chennai

Row ID 68 TURNER Administration Mumbai

Row ID 70 WARD Marketing Mumbai

Structure of Index Organized Table


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Structure of Clustered Vs Non-Clustered Table


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Different types of Index

B-Tree

Bitmap

Functional

Reverse


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ADAMS ROW ID 6

ALLEN ROW ID 11

BLAKE ROW ID 5

CLARK ROW ID 14ADAMS Block 3

FORD Block 6

Structure of B-Tree Index

ADAMS Block 1MARTIN Block 2

MARTIN Block 5

SMITH Block 4

FORD ROW ID 1

JAMES ROW ID 4

JONES ROW ID 2

KING ROW ID 10

MARTIN ROW ID 12

MILLER ROW ID 3

SCOTT ROW ID 7

SMITH ROW ID 8

TURNER ROW ID 9

WARD ROW ID 13Branch Block

Leaf Block

Root Block


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Chennai201400

1010101010101111111111111000000011000000

00000000001110001111

11111111111000011111

11001111111111111111

10101010101011111111

11111000000011000000

00000000001110001111

11111111111000011111

11001111111111111111

Structure of Bitmap Index

Mumbai201400

0100010001010000000000000111111100111111

11100000110001110000

00000000000111100000

00110000000000000000

01010101010100000000

00000111111100111111

11111111110001110000

00000000000111100000

00110000000000000000

Column

value

Starting

RowID

Ending

RowID

Bitmap of

Column

values


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adams ROW ID 6

allen ROW ID 11

blake ROW ID 5

clark ROW ID 14adams Block 3

ford Block 6

Structure of Function Based Index

adams Block 1martin Block 2

martin Block 5

smith Block 4

ford ROW ID 1

james ROW ID 4

jones ROW ID 2

king ROW ID 10

martin ROW ID 12

miller ROW ID 3

scott ROW ID 7

smith ROW ID 8

turner ROW ID 9

ward ROW ID 13

Branch Block

Leaf Block

Root Block

Function based index on

lower(name)


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196 Block 3

200 Block 6

196 ROW ID 12

197 ROW ID 3

198 ROW ID 7

199 ROW ID 76

200 ROW ID 8

201 ROW ID 9

202 ROW ID 13

203 ROW ID 19

196 Block 1

Normal Index

Reverse key Index Compared with Normal Index

Normal Reverse

196 691197 791

198 891

199 991

200 002

201 102

202 202

203 302

002 ROW ID 8

051 ROWID 201

090 ROWID 11

102 ROW ID 19002 Block 3

691 Block 6002 Block 1

200 ROW ID 120

202 ROW ID 43

350 ROW ID 27

Reverse key Index

691 ROW ID 12

695 ROWID 2

Normal key converted

to Reverse key

Note:- Reverse key indexes are used to prevent index blocks become hotspot. ie. Normal

index stores consecutive values adjacently. In reverse key index the values are reversed to

scatter while storing. So, reverse key index does not support range scans.


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Indexing

Consider indexing keys that are used frequently in WHERE

clauses. Consider indexing keys that are used frequently to join tables in

SQL statements

Index keys that have high selectivity. The selectivity of an index

is the percentage of rows in a table having the same value for theindexed key.

Consider indexing foreign keys of referential integrity

constraints

Do not modify indexed columns with functions such as RTRIM,TO_CHAR, UPPER, TRUNC as this will disable the index.

Instead perform the modification on the constant side of the

condition.


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Composite IndexWhen?

Improved selectivity

Two or more columns or expressions, each withpoor selectivity, can be combined to form a composite

index with higher selectivity.

Reduced I/O

If all columns selected by a query are in acomposite index, then Oracle can return these valuesfrom the index without accessing the table.


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INDEX - Facts

Too many indexes will slowdown data manipulation.

Unique indexes are more efficient than non-unique indexes.

NULL values are not indexed in B-tree but in bitmap.

Index based access are not always fast.


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What is a View?

A view is stored query that may refer one or more of the table

column and table rows.

The view can be used to ease the work of the programmer /user.

The view can be used to secure the data.

EMPNO ENAME JOB SAL

----- ------ ------- -----

7788 SCOTT ANALYST 3000

7902 FORD ANALYST 3000

7369 SMITH CLERK 800

7876 ADAMS CLERK 1100

7934 MILLER CLERK 1300

7900 JAMES CLERK 950

7566 JONES MANAGER 2975

7782 CLARK MANAGER 2450

7698 BLAKE MANAGER 2850

Select empno, ename

From emp

Where job=CLERK


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SQL> select * from emp;

EMPNO ENAME JOB MGR HIREDATE SAL DEPTNO

----- -------- --------- ----- --------- ----- ------

7369 SMITH CLERK 7902 17-DEC-80 800 20

7499 ALLEN SALESMAN 7698 20-FEB-81 1600 30

7566 JONES MANAGER 7839 02-APR-81 2975 20

7654 MARTIN SALESMAN 7698 28-SEP-81 1250 30

7698 BLAKE MANAGER 7839 01-MAY-81 2850 30

7788 SCOTT ANALYST 7566 19-APR-87 3000 20

7839 KING PRESIDENT 17-NOV-81 5000 10

7844 TURNER SALESMAN 7698 08-SEP-81 1500 30

7876 ADAMS CLERK 7788 23-MAY-87 1100 20

7900 JAMES CLERK 7698 03-DEC-81 950 30

7934 MILLER CLERK 7782 23-JAN-82 1300 10

ViewSample table


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SQL> create view clerk_listasSelect empno, enameFrom empWhere job='CLERK' ;

SQL> select * from clerk_list;EMPNO ENAME

---------- ----------7369 SMITH7876 ADAMS7934 MILLER7900 JAMES

SQL> select * from clerk_list where ename like 'S%';EMPNO ENAME

---------- ----------7369 SMITH

ViewExample


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Nested Loops

Sort Merge

Hash Join

JOINS - Methods


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Architecture The profession of designing buildings and environments

with consideration for their esthetic effectCarex Large genus of plants found in damp woodlands and

bogs and ditches or at water margins.

Integrity Moral soundness; honesty; freedom from corrupting

influence or motive;

Intellect the power to judge and comprehend; the thinking

faculty; the understanding.

Linux An open-source version of the UNIX operating system

Oracle The communications, revelations, or messages

delivered by God to the prophets; also, the entiresacred Scriptures.

Polarimeter An instrument for determining the amount of

polarization of light, or the proportion of polarized light,

in a partially polarized ray.

PolarisThe polestar. See North star, under North.

Polariscope An instrument consisting essentially of a polarizer and

an analyzer, used for polarizing light, and analyzing its

properties.

Steeple A tall tower that forms the superstructure of a building

Nested Loop

select ox.*

from my_words mw,

oxford oxwhere mw.word=ox.word ;

my_words (Driving table) oxford (Driven table)

The small table is taken as driving table.

The bigger indexed table is taken as the driven table

PolarisOracle

Linux

Steeple


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Polaris RowID 5

Infosys RowID 20Wipro RowID 7

Infosys RowID 20

Polaris RowID 5

Wipro RowID 7

Polaris RowID 3

Wipro RowID 37

Oracle RowID 202

IBM RowID 34

IBM RowID 34

Oracle RowID 202

Polaris RowID 3

Wipro RowID 37

IBM RowID 34 Table B

Infosys RowID 20 Table A

Oracle RowID 202 Table B

Polaris RowID 5 Table A

Polaris RowID 3 Table B

Wipro RowID 7 Table A

Wipro RowID 37 Table B

Sort Merge

Sorting Sorting

Merging

Table A Table B


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Apply Hash

Function to

Build Hash

Table

Big Table

Small Table

Hash Join

Hash Table

Apply

Hash

Function


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Joins

Nested loops usually offer better response time. Effective onlywhen the index is available and can be used.

The larger the subset of rows being processed, the more likelythat a sort merger or hash join will be faster.

Recommendations:

Both tables A and B are smallsort merge/hash

Only selecting a small sub-set of rows from Bnested loops Want the first row as quickly as possiblenested loops

Getting rows from A by an index lookupsort merge/hash


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Choosing Join Plans

CBO decides the following for all the joins:

Access path

Full scan, Index range scan, Index fast full scan, etcJoin Method

Nested loop, Sort merge, Hash join

Join Order

Which table will drive the join process


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Thank You

Schemas Tables Indexes Views and JoOracle Objects ins

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